Self-supervised representation learning
aims at learning high-quality intermediate features to generalize to other downstream tasks.
To this end, various annotation-free pretext tasks have been proposed, e.g., rotation prediction [1]}, solving jigsaw puzzle [2]}, image colorization [3]}, and image inpainting [4]}.
Pseudo labeling also has been widely used as it allows to learn models using conventional losses; existing methods in this direction discover pseudo classes by clustering [5]} or treat each training instance as a surrogate class [6]}, [7]}, [8]}, [9]}.
Also, as an extension of the surrogate class, nearest neighbors have been leveraged as a positive set for self-supervised learning [10]}, [11]}, [12]}, but they often produce noisy labels unless a sufficiently trained embedding space is provided in advance.
